Engine fault diagnosis based on a morphological neural network using a morphological filter as a preprocessor

Feature extraction and faults classification are the two most significant issues involved in the field of mechanical fault diagnosis problems. This work addresses these two problems using mathematical morphology and non-negative matrix factorization. In particular, the authors present a novel engine fault diagnosis scheme utilizing the averaged multi-scale morphological filter to enhance the vibration signals, non-negative matrix factorization to characterize the signals, and a constructive morphological neural network to classify the engine operating states. Eight engine running states including the healthy state and seven defective states are tested in an engine experiment rig to evaluate the presented fault diagnosis scheme. Conventional feature extraction methods as well as classifiers popularly used in the literature are also employed as a comparison. The experimental results indicate the proposed approach to be an effective and efficient scheme for detection of the intelligent faults of engines.

• Record URL:
  https://doi.org/10.1177/0954407012457899
• Record URL:
  http://pid.sagepub.com/cgi/content/abstract/227/4/490
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/09544070
• Supplemental Notes:
  • Reprinted by permission of Sage Publications, Ltd.
• Authors:
  • Li, Bing
  • Hu, Ren-Xi
  • Ren, Guo-Quan
  • Fu, Jian-Ping
• Publication Date: 2013-4

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 490-505
• Serial:
  • Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering
  • Volume: 227
  • Issue Number: 4
  • Publisher: Sage Publications Limited
  • ISSN: 0954-4070
  • EISSN: 2041-2991
  • Serial URL: http://pid.sagepub.com/content/current

Subject/Index Terms

• TRT Terms: Defects; Diagnostic tests; Engine performance; Engines; Equipment maintenance; Fault monitoring; Mechanical failure; Neural networks
• Subject Areas: Highways; Vehicles and Equipment; I95: Vehicle Inspection;

Filing Info

• Accession Number: 01483352
• Record Type: Publication
• Files: TRIS
• Created Date: Jun 6 2013 9:35AM